Virtual Visual Servoing for Multicamera Pose Estimation

Exploiting multiple cameras for object pose estimation contributes toward achieving enhanced accuracy and robustness to measurement faultiness. Nonlinear optimization frameworks, such as the Gauss-Newton, were traditionally employed for this purpose. This paper proposes a novel algorithm for multicamera pose estimation by using the concept of virtual visual servoing. Two fusion structures, namely centralized and decentralized fusion, are considered. The former offers higher accuracy at the price of increased computation. The latter improves the computation speed, partially sacrificing the system accuracy. The experimental results imply the proposed configurations to be faster than the Gauss-Newton pose estimation method, while offering the same or even higher level of accuracy.